Upscaling surface energy fluxes over the North Slope of Alaska using airborne eddy-covariance measurements and environmental response functions

Serafimovich, Andrei; Metzger, Stefan; Hartmann, Jörg; Kohnert, Katrin; Zona, Donatella; Sachs, Torsten

doi:https://doi.org/10.5194/acp-18-10007-2018

Articles | Volume 18, issue 13

https://doi.org/10.5194/acp-18-10007-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/acp-18-10007-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 18, issue 13

Research article

|

13 Jul 2018

Research article |

| 13 Jul 2018

Upscaling surface energy fluxes over the North Slope of Alaska using airborne eddy-covariance measurements and environmental response functions

Andrei Serafimovich, Stefan Metzger, Jörg Hartmann, Katrin Kohnert, Donatella Zona, and Torsten Sachs

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Final revised paper (published on 13 Jul 2018)
Preprint (discussion started on 19 Mar 2018)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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RC1: 'Referee Comment: Discussion Paper acp-2017-1166', Anonymous Referee #1, 25 Apr 2018
- AC1: 'Response to reviewer #1', Andrei Serafimovich, 14 Jun 2018
RC2: 'Referee report', Anonymous Referee #2, 17 May 2018
- AC2: 'Response to reviewer #2', Andrei Serafimovich, 14 Jun 2018

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Andrei Serafimovich on behalf of the Authors (14 Jun 2018) Author's response Manuscript

ED: Publish as is (15 Jun 2018) by Geraint Vaughan

AR by Andrei Serafimovich on behalf of the Authors (15 Jun 2018) Author's response

Short summary

In order to support the evaluation of coupled atmospheric–land-surface models we investigated spatial patterns of energy fluxes in relation to land-surface properties and upscaled airborne flux measurements to high resolution flux maps. A machine learning technique allows us to estimate environmental response functions between spatially and temporally resolved flux observations and corresponding biophysical and meteorological drivers.